Senior Data Scientist
Hybrid Ciudad de México Full-time Senior Data Science
At a glance
Senior Data Science role at Fa Ewjt Saasfaprod1. Ciudad de México · full-time.
Pay not stated Data Scientist salary
RoleData Science
SenioritySenior
LocationCiudad de México
WorkplaceHybrid
EmploymentFull-time
PostedOct 28, 2026
Role brief
dataroles summary, based on the employer's posting.
What you'll do
- Build knowledge graph models and semantic structures to map relationships and meaning
- Use data mining to surface trends, unusual activity and hidden connections for business and risk decisions
- Create pipelines that prepare, enrich and validate data, then work with engineers to deliver models and graph services
- Explain results to technical and non-technical audiences while keeping data quality, traceability and governance in view
- Use LLM-supported analysis and graph AI, checking outputs for bias, drift, leakage and reliability
What you bring
- A bachelor's or master's degree in engineering
- Two to five years of relevant experience
Who this fits
This role suits a senior data scientist comfortable combining graph analysis, data pipelines and AI-assisted investigation. It is based in Ciudad de México and follows a hybrid arrangement.
From the employer
Role Summary:
Develop advanced analytics and graph-based intelligence solutions to uncover patterns, relationships, and actionable insights for business and risk use cases.
Key Responsibilities:
- Design and implement knowledge graph models and semantic structures.
- Apply data mining techniques to detect trends, anomalies, and hidden links.
- Build data preparation, enrichment, and validation pipelines.
- Collaborate with engineering teams to produce models and graph services.
- Communicate findings clearly to technical and non-technical stakeholders.
- Ensure model/data traceability, quality, and governance alignment.
AI Skills for This Role:
- Use LLM-assisted workflows for hypothesis generation and analysis acceleration.
- Apply AI methods for graph reasoning, entity linking, and relationship discovery.
- Evaluate model outputs critically for bias, drift, leakage, and reliability.
- Use AI tools responsibly with strong data privacy and governance practices.
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